Systems and Methods for Analyzing Boilerplate

ABSTRACT

Systems and methods for analyzing boilerplate are described. In one described system, an indexer identifies a common element in a plurality of related articles. The indexer then classifies the common element as boilerplate. For example, the indexer may identify a copyright notice appearing in a plurality of related articles. The copyright notice in these articles is considered boilerplate.

RELATED APPLICATIONS

This application is a continuation of prior application Ser. No.10/815,150, filed Mar. 31, 2004, which is incorporated by referenceherein.

This application relates to:

U.S. application Ser. No. 10/814,908, filed Mar. 31, 2004, titled“Systems and Methods for Generating Multiple Implicit Search Queries”;

U.S. application Ser. No. 10/814,871, filed Mar. 31, 2004, titled“Systems and Methods for Extracting a Keyword from an Event”;

U.S. application Ser. No. 10/815,074, filed Mar. 31, 2004, titled“Systems and Methods for Weighting a Search Query Result”;

U.S. application Ser. No. 10/814,056, filed Mar. 31, 2004, titled“Systems and Methods for Refreshing a Content Display”;

U.S. application Ser. No. 10/814,368, filed Mar. 31, 2004, titled“Systems and Methods for Constructing and Using a User Profile”;

U.S. application Ser. No. 10/814,365, filed Mar. 31, 2004, titled“Systems and Methods for Identifying a Named Entity”;

U.S. application Ser. No. 10/814,053, filed Mar. 31, 2004, titled“Systems and Methods for Associating a Keyword with a User InterfaceArea”;

U.S. application Ser. No. 10/813,875, filed Mar. 31, 2004, titled“Systems and Methods for Ranking Implicit Search Results”;

U.S. application Ser. No. 10/814,052, filed Mar. 31, 2004, titled“Systems and Methods for Generating a User Interface”; and

U.S. application Ser. No. 10/814,924, filed Mar. 31, 2004, titled“Systems and Methods for Providing Search Results,”

the entirety of all of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to methods and systems forinformation retrieval. The present invention relates particularly tosystems and methods for analyzing boilerplate.

BACKGROUND

Conventional search engines receive a search query from a user andexecute a search against a global index. Such conventional searchengines typically use one or more conventional methods for performing asearch. For example, one known method, described in an article entitled“The Anatomy of a Large-Scale Hypertextual Search Engine,” by SergeyBrin and Lawrence Page, assigns a degree of importance to a document,such as a web page, based on the link structure of the web. The searchresults are often presented in a list format, comprising articleidentifiers and brief snippets about the documents in a web page thatcan be resized.

Often, the user has access to other information stored on the user'slocal machine or on other storage media accessible via a network that isrelevant to the user's current contextual state. For example, if a useris working on a document regarding a particular subject, informationabout the subject may be stored on the user's hard drive or in a globalindex accessible to the user. In order to access this information, theuser issues an explicit search query in an application, such as a websearch page. The information is provided to the user as a result set.Thus, the user shifts focus from the document that the user is workingon to perform the search.

In many cases, the user may be unaware or may not remember thatinformation is available regarding a particular subject. In such a case,the user may not perform an explicit search and thus, will not haveaccess to the potentially relevant information.

SUMMARY

Embodiments of the present invention provide systems and methods foranalyzing boilerplate. In one embodiment, a computer program, such as anindexer, identifies a common element in a plurality of related articles.The indexer then classifies the common element as boilerplate. Forexample, in one embodiment, the indexer identifies a copyright noticeappearing in a plurality of related articles. The copyright notice inthese articles is considered boilerplate.

These exemplary embodiments are mentioned not to limit or define theinvention, but to provide examples of embodiments of the invention toaid understanding thereof. Exemplary embodiments are discussed in theDetailed Description, and further description of the invention isprovided there. Advantages offered by the various embodiments of thepresent invention may be further understood by examining thisspecification.

BRIEF DESCRIPTION OF THE FIGURES

These and other features, aspects, and advantages of the presentinvention are better understood when the following Detailed Descriptionis read with reference to the accompanying drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary environment in whichone embodiment of the present invention may operate;

FIG. 2 is a flowchart illustrating a method for extracting keywords froman event in one embodiment of the present invention;

FIG. 3 is a flowchart illustrating a method of executing multiplequeries to return results as relevant to the user's context in oneembodiment of the present invention;

FIG. 4 is a flowchart illustrating a method for ranking combined resultsin one embodiment of the present invention;

FIG. 5 is a flowchart illustrating the method for processing a query inone embodiment of the present invention;

FIG. 6 is a flowchart illustrating a method for identifying parts ofspeech in one embodiment of the present invention;

FIG. 7 is a flowchart illustrating a method for altering a relevancescore in one embodiment of the present invention;

FIG. 8 is a flowchart illustrating a method of varying a refreshthreshold in one embodiment of the present invention;

FIG. 9 is a flowchart illustrating a method of varying a relevance scorefor a result based on a user's click-through behavior in one embodimentof the present invention;

FIG. 10 is a flowchart illustrating a method for displaying implicitquery results in one embodiment of the present invention;

FIG. 11 is a flowchart illustrating a method according to one embodimentof the present invention for modifying a search query and thecorresponding result set based on the search query, the modificationsbased at least in part of an attribute stored in a user profile; and

FIG. 12 is a flowchart illustrating a method for identifying boilerplateand content in one embodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention provide systems and methods foranalyzing boilerplate. Exemplary embodiments are described below.Referring now to the drawings in which like numerals indicate likeelements throughout the several figures, FIG. 1 is a block diagramillustrating an exemplary environment for implementation of anembodiment of the present invention. While the environment shownreflects a client-side search engine architecture embodiment, otherembodiments are possible.

The system 100 shown in FIG. 1 includes multiple client devices 102 a-nin communication with a server device 150 over a wired or wirelessnetwork 106. The network 106 shown comprises the Internet. In otherembodiments, other networks, such as an intranet, may be used instead.Moreover, methods according to the present invention may operate withina single client device.

The client devices 102 a-n shown each includes a computer-readablemedium 108. The embodiment shown includes a random access memory (RAM)108 coupled to a processor 110. The processor 110 executescomputer-executable program instructions stored in memory 108. Suchprocessors may include a microprocessor, an ASIC, a state machine, orother processor, and can be any of a number of computer processors, suchas processors from Intel Corporation of Santa Clara, Calif. and MotorolaCorporation of Schaumburg, Illinois. Such processors include, or may bein communication with, media, for example computer-readable media, whichstores instructions that, when executed by the processor, cause theprocessor to perform the steps described herein.

Embodiments of computer-readable media include, but are not limited to,an electronic, optical, magnetic, or other storage or transmissiondevice capable of providing a processor, such as the processor 110 ofclient 102 a, with computer-readable instructions. Other examples ofsuitable media include, but are not limited to, a floppy disk, CD-ROM,DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, a configuredprocessor, all optical media, all magnetic tape or other magnetic media,or any other medium from which a computer processor can readinstructions. Also, various other forms of computer-readable media maytransmit or carry instructions to a computer, including a router,private or public network, or other transmission device or channel, bothwired and wireless. The instructions may comprise code from any suitablecomputer-programming language, including, for example, C, C++, C#,Visual Basic, Java, Python, Perl, and JavaScript.

Client devices 102 a-n can be connected to a network 106 as shown, orcan be stand-alone machines. Client devices 102 a-n may also include anumber of external or internal devices such as a mouse, a CD-ROM, DVD, akeyboard, a display, or other input or output devices. Examples ofclient devices 102 a-n are personal computers, digital assistants,personal digital assistants, cellular phones, mobile phones, smartphones, pagers, digital tablets, laptop computers, Internet appliances,and other processor-based devices. In general, the client devices 102a-n may be any type of processor-based platform that operates on anyoperating system, such as Microsoft® Windows® or Linux, capable ofsupporting one or more client application programs. For example, theclient device 102 a shown comprises a personal computer executing clientapplication programs, also known as client applications 120. The clientapplications 120 can be contained in memory 108 and can include, forexample, a word processing application, a spreadsheet application, anemail application, an instant messenger application, a presentationapplication, an Internet browser application, a calendar/organizerapplication, and any other application capable of being executed by aclient device.

The user 112 a can interact with the various client applications 120 andarticles associated with the client applications 120 via various inputand output devices of the client device 102 a. Articles include, forexample, word processor, spreadsheet, presentation, email, instantmessenger, database, and other client application program content filesor groups of files, web pages of various formats, such as HTML, XML,XHTML, Portable Document Format (PDF) files, and audio files, videofiles, or any other documents or groups of documents or information ofany type whatsoever.

The memory 108 of the client device 102 a shown also contains a captureprocessor 124, a queue 126, and a search engine 122. The client device102 a shown also contains or is in communication with a data store 140.The search engine 122 can receive an explicit query from the user 112 aor generate an implicit query and retrieve information from the datastore 140 in response to the query.

The search engine 122 shown contains an indexer 130, a query system 132,and a formatter 134. Events, real-time and historical, contextual andindexable, and performance data can be sent by the queue 126 to thequery system 132 to provide the query system 132 with informationconcerning the current user context. The query system 132 can use thisinformation to generate an implicit query. The query system 132 can alsoreceive and process explicit queries from the user 112 a.

The data store 140 can be any type of computer-readable media and can beintegrated with the client device 102 a, such as a hard drive, orexternal to the client device 102 a, such as an external hard drive oron another data storage device accessed through the network 106. Thedata store 140 may include any one or combination of methods for storingdata, including without limitation, arrays, hash tables, lists, andpairs.

In the embodiment shown in FIG. 1, a user 112 a can input an explicitquery into a search engine interface displayed on the client device 102a, which is received by the search engine 122. The search engine 122 canalso generate an implicit query based on a current user context orstate, which can be determined by the query system 132 from contextualreal time events. Based on the query, the query system 132 can locaterelevant information in the data store 140 and provide a result set.

The data store 140 comprises a local index. The local index in theembodiment shown in FIG. 1 may comprise information, such as articles,which are associated with the client device 102 a, a user 112 a of theclient device 102 a, or a group of users of the client device 102 a. Forexample, the local index in the data store 140 shown in FIG. 1 maycomprise an index of articles created, edited, received, or stored bythe client user 112 a using the client machine 102 a, or articlesotherwise associated with the client user 102 a or the client machine112 a. The local index may be stored in a client machine, such as indata store 140, in a data store on a local network in a manneraccessible by the client machine, on a server accessible to the clientmachine through the Internet, or in another accessible location.

In contrast, a global index may comprise information relevant to manyusers or many servers, such as, for example, an index of web pageslocated on multiple servers in communication with the World Wide Web.One example of a global index is an index used by the Google(™) searchengine to provide search results in response to a search query.

A single index may comprise both a local and a global index. Forexample, in one embodiment, an index may comprise both local and globalinformation, and include a user or client identifier with the localinformation so that it may be identified with the user(s) or client(s)to which it pertains. Moreover, an index, local or global, may bepresent in one or multiple logical or physical locations.

In one embodiment, the result set comprises article identifiersidentifying articles associated with the client applications 120 orclient articles. Client articles stored in the data store 140 includearticles associated with the user 112 a or client device 102 a, such asthe word processing documents, previously viewed web pages and any otherarticle associated with the client device 102 a or user 112 a. Inanother embodiment, the result set also comprises identifiersidentifying articles located on the network 106 or network articleslocated by a search engine on a server device. Network articles includearticles located on the network 106 not previously viewed or otherwisereferenced by the user 112 a, such as web pages not previously viewed bythe user 112 a.

The result sets comprise one or more article identifiers. An articleidentifier may be, for example, a Uniform Resource Locator (URL), a filename, a link, an icon, a path for a local file, or anything else thatidentifies an article. In the embodiment shown, an article identifiercomprises a URL associated with an article.

Messaging articles stored in the data store 140 include user's emails,chat messages, and instant messaging messages. Each time a message isreceived, sent, modified, printed, or otherwise accessed, a record isstored in the data store 140. This information can later be searched toidentify messages that should be displayed in the user interface.

An embodiment of the present invention may also store message threads inthe data store 140. In such an embodiment, messages are related togetherby various attributes, including, for example, the sender, recipient,date/time sent and received, the subject, the content, or any otherattribute of the message. The related messages can then be retrieved asa thread, which may be treated as a document by the display processor128.

The formatter 134 can receive the search result set from the querysystem 132 of the search engine 122 and can format the results foroutput to a display processor 128. In one embodiment, the formatter 134formats the results in XML or HTML. The display processor 128 can becontained in memory 108 and can control the display of the result set ona display device associated with the client device 102 a. The displayprocessor 128 may comprise various components. For example, in oneembodiment, the display processor 128 comprises a Hypertext TransferProtocol (HTTP) server that receives requests for information andresponds by constructing and transmitting Hypertext Markup Language(HTML) pages. In one such embodiment, the HTTP server comprises ascaled-down version of the Apache Web server. In various embodiments,the functions described herein may be performed by various othercomponents and devices.

Through the client devices 102 a-n, users 112 a-n can communicate overthe network 106, with each other and with other systems and devicescoupled to the network 106. As shown in FIG. 1, a server device 150 isalso coupled to the network 106. In the embodiment shown, the searchengine 122 can transmit a search query comprised of an explicit orimplicit query or both to the server device 150. The user 112 a can alsoenter a search query in a search engine interface, which can betransmitted to the server device 150. In another embodiment, the querysignal may instead be sent to a proxy server (not shown), which thentransmits the query signal to server device 150. Other configurationsare also possible.

The server device 150 shown includes a server executing a search engineapplication program, such as the Google™ search engine. Similar to theclient devices 102 a-n, the server device 150 shown includes a processor160 coupled to a computer-readable memory 162. Server device 150,depicted as a single computer system, may be implemented as a network ofcomputer processors. Examples of a server device 150 are servers,mainframe computers, networked computers, a processor-based device, andsimilar types of systems and devices. The server processor 160 can beany of a number of computer processors, such as processors from IntelCorporation of Santa Clara, California and Motorola Corporation ofSchaumburg, Ill.

Memory 162 contains the search engine application program, also known asa search engine 170. The search engine 170 locates relevant informationin response to a search query from a client device 102 a. The searchengine 122 then provides the result set to the client device 102 a viathe network 106. The result set 134 comprises one or more articleidentifiers. An article identifier may be, for example, a URL, a filename, a link, an icon, a path for a local file, or anything else thatidentifies an article. In the embodiment shown, an article identifiercomprises a URL associated with an article.

In the embodiment shown, the server device 150, or related device, haspreviously performed a crawl of the network 106 to locate articles, suchas web pages, stored at other devices or systems connected to thenetwork 106, and indexed the articles in memory 162 or on another datastorage device.

It should be noted that the present invention may comprise systemshaving different architecture than that which is shown in FIG. 1. Forexample, in some systems according to the present invention, serverdevice 104 may comprise a single physical or logical server. The system100 shown in FIG. 1 is merely exemplary, and is used to explain theexemplary methods shown in FIGS. 2 through 11.

Various methods may be implemented in the environment shown in FIG. 1and other environments, according to the present invention. Methodsaccording to the present invention may be implemented by, for example, aprocessor-executable program code stored on a computer-readable medium.

In one embodiment, the indexer 130 identifies a boilerplate element inan article comprising the boilerplate element and a content element, andgenerates an implicit search query comprising a search term, the searchterm comprising a term present in the content element.

In another embodiment, the query system 132, removes or downweightsboilerplate when an explicit query is issued by the user. In the case ofboth implicit and explicit queries, the relative importance of contentis given higher weight than the boilerplate. In one such embodiment, thearticle is indexed after the boilerplate has been removed; the resultantweighting may be more accurate since it relies relatively more heavilyon non-boilerplate.

Boilerplate includes, for example, headers, footers, and navigationalelements that may occur on multiple articles. In one embodiment,boilerplate is identified based on analysis of a plurality of relatedarticles, for example multiple web pages within a web site. In anotherembodiment, boilerplate is identified based on analysis of a singlearticle.

The indexer 130 may identify a boilerplate element in various ways. Inone embodiment, the indexer 130 analyzes the frequency of terms andphrases in a plurality of related article to identify a common element.The indexer 130 then classifies the common element as boilerplate. Forexample, in one embodiment, the indexer 130 identifies the phrase“Copyright 2004,” a copyright notice appearing in a plurality of relatedarticles, as boilerplate.

Common terms and phrases occurring on multiple related pages may not beboilerplate. For example, a site about astronomy may include the term“astronomy” in many or all pages, and that term is relevant andimportant. Accordingly, an embodiment of the present invention maycombine the method described above for identifying boilerplate withother methods.

In one embodiment of the present invention, the indexer 130 considersthe spatial location of terms or phrases within an article. Common termsor phrases that occur often at a particular position in an article maybe boilerplate. For example, a common term or phrase that occurs at thebottom of an article is often a copyright notice or other commonelement.

Similarly, common terms or phrases that occur in the same position atthe top, left, and right of an article may be navigational elements. Forweb sites, navigational elements are links that allow a user to go tocertain sections of the site; for example, many pages on a web site maycontain a link to the home page, a help page, and other sections withina site.

In another embodiment, the indexer 130 considers article markup nearby acommon term or phrase. For example, navigational elements typicallycontain markup for the links, and may contain code such as JavaScript.For example, JavaScript code is often used in navigational elements toalter the appearance of the element when the user moves the mouse overthe element. In one such embodiment, a score is determined for commonterms and phrases that considers markup nearby. Different kinds ofmarkup may have different weights. For example, links or JavaScript maycontribute higher weight than bold or italics. Some markup can reliablyidentify boilerplate even with only one article, i.e., it is notnecessary to restrict analysis to common items across multiple articles.The destination of links can be used to identify certain elements, forexample links that go to the home page of a site or a page ending in“help.html” or “copyright.html” may be considered boilerplate.

In one embodiment, boilerplate is identified based on a predeterminedlist of terms and phrases. For example, a list of common navigational orlegal terms, such as “Home”, “Help”, “Terms of Service”, and “Copyright”is used, and sections of an article containing these terms areconsidered boilerplate. The sections may, for example, be sentences orparagraphs. Often these terms can match text that is not boilerplate. Inanother similar embodiment, the indexer also uses the position of thetext on the page, e.g. a sentence containing the term “Copyright” thatis also the last line on a page, or that occurs on multiple articleswithin a set of articles, or both, is more likely to be consideredboilerplate. In one embodiment, boilerplate text is not indexed.

In another embodiment, the indexer 130 identifies a term having a lowinverse document frequency measure. In other words, the indexer 130identifies a term that appears often in many different articles. Suchterms are more likely to be boilerplate than terms that rarely appear.Examples of such terms are “home” and “contact us.” These terms appearas hyperlinks on many pages available on the Internet.

In one embodiment of the present invention, text in an article isassigned a weight indicating the probability that the text isboilerplate. This weight may be used by the query system 132 to alterthe ranking when the text matches a query.

Embodiments of the present invention are capable of generating implicitqueries based on a user's contextual state. The results of an implicitquery are displayed to the user in a content display window. The resultsmay be updated periodically as the user's contextual state changes. Forexample, in one embodiment, the user is working on a word documentconcerning budgeting. A query implicit builder (“QUIB”), one componentof the query system 132 shown in FIG. 1, requests and receives eventsrelated to the document. The QUIB generates queries from the events andpresents the results of the queries to the user.

Events comprise historical, contextual, and real-time events. In oneembodiment, contextual events are time sensitive. Contextual eventsrelate to actions that are occurring now or have occurred within a shorttime frame, e.g., the last ten words that the user typed. In contrast,real-time events are less time-sensitive and may be of highersignificance even after an elapsed period of time, e.g., the userprinted or opened a file.

Events may be tracked over multiple sessions. For example, in oneembodiment, if a user has opened a web page repeatedly during the lastseveral times the user has used a client machine, the query system 132tracks the usage for each of those sessions by tracking the eventsassociated with the usage. In one such embodiment, access during aparticular session is downweighted or promoted based on the period oftime that has elapsed since the session. In other words, eventsassociated with more recent accesses of a specific page are weightedmore heavily than those occurring less recently.

The events may include information, such as the last twenty words theuser typed, the last sentence the user typed, the text nearby the cursor(e.g., the text up to x words before and y words after), the currentlyactive buffer (e.g., the entire active document), the selected orhighlighted buffer, the buffer in the clipboard, or other informationrelevant to the user's context. The query system 132 extracts keywordsfrom the information and generates a search query to be submitted to asearch engine. The query system 132 creates and executes the query as ifthe user had explicitly typed the keywords in a search interface.

In one embodiment, the query system 132 learns from a user's behaviorwhether or not certain data streams or keywords are particularlyrelevant. The query system 132 may rely on click-throughs within thecontent display window to determine results in which the user exhibitsparticular interest. For example, if the content display includes a linkthat has been shown to a user multiple times but has not been clicked,the link may be eliminated from the content display. The data streams,query types, or keywords that resulted in the link being displayed maybe downweighted in subsequent analysis. In contrast, if the user clicksthe link, this typically indicates that the user was interested in thearticle, and can result in promoting the data streams, query types, orkeywords that resulted in the link being displayed. These data streams,query types, or keywords would then be used with increased weight insubsequent analysis.

The query system 132 shown in FIG. 1 utilizes multiple data streams assources for generating search queries. For example, if the user isediting a document, the query system 132 may use the last 20 words thatwere typed, as well as the entire document to extract keywords andgenerate search queries. The query system 132 generates a search queryfor each data stream and combines the result sets corresponding to eachsearch query for display to the user.

FIG. 2 is a flowchart illustrating a method for extracting keywords froman event in one embodiment of the present invention. In the embodimentof the present invention shown in FIG. 1, the query system 132 comprisesa query implicit builder (“QUIB”). The query system 132 creates searchqueries based on the user's current contextual state. The query system132 first receives a contextual event 202. The contextual event is anoccurrence that is captured by the capture processor 124 and may be usedto either update the user's contextual state and/or may be indexed andstored in the event database in data store 140 to provide informationfor future queries.

The query system 132 extracts keywords from the event in order togenerate one or more search queries. The keywords may comprise wordsthat the user has recently typed, words that occur in a document orbuffer, or may comprise any other type of keyword that the system isable to identify. The keywords may comprise all of the words in theevent. The query system 132 may extract a keyword from any of a numberof data streams. For example, one embodiment of the present inventionmay use one or more of the following to extract implicit queries: (1)the most recently typed n words where n is on the order of ten; (2) then words around the user's cursor where n is in the order of ten (e.g., xwords before the cursor and y words after the cursor); (3) words in thecurrent selection; (4) words from the current document (e.g., one suchmethod selects the most frequently occurring words); (5) previousexplicit queries executed by the user or submitted by the user; (6)clipboard content; (7) a list of all the names of people with which theuser has communicated; (8) a list of email addresses and/or instantmessenger “buddy names”; and (9) a list of important terms or phrasesfor the user.

For example, in one embodiment of the present invention, the querysystem 132 extracts keywords from an entire buffer, e.g., an entireMicrosoft® Word document. In another embodiment of the presentinvention, the query system 132 extracts keywords based on a termfrequency (“TF”). The term frequency refers to how frequently a termoccurs within a document or within a data stream. In another embodiment,the query system 132 extracts the inverse document frequency (“IDF”),which is defined as the inverse of how often a term appears in documentsin general. For example, the term “budget” may appear in a particulardocument twenty times. The TF of the term “budget” would be higher thanthe TF of a word that only occurs once in the document. The IDF of theterm “budget” is also likely to be relatively high since the term isunlikely to appear in many documents in general. In contrast, the word“the” is likely to have a very high TF and a very low IDF since “the”occurs frequently in many documents and data streams.

In another embodiment of the present invention, the query systemreceives explicit queries that are captured by an application on theclient 102 a, such as a Winsock Layered Service Provider (“LSP”). Whenthe user submits a query to a global index, such as the Google™ searchengine, the Winsock LSP captures the query as an event and provides aquery, either the original or a modified version, to another searchengine application, such as search engine 122 on the client 102 a. Thelocal search engine 122 processes the query substantially simultaneouslywith the global search engine. When the query system 132 receivesresults from both search engines, it combines the results and creates auser interface element comprising the combined results.

In another embodiment of the present invention, the query system 132notifies the display processor 128 when new results are ready. Thedisplay processor 128 issues an HTTP request to an HTTP server (notshown), which returns a result set corresponding with the implicit queryto the display processor.

Other methods for extracting keywords from data streams may be utilizedby in alternative embodiment of the present invention. For example, thequery system 132 may use identified terms to generate search queries. Anidentified term is a term that has been noted as being particularlyrelevant to the user's contextual state. For instance, an identifiedterm may comprise the name of a person to which the user recentlydirected an email. The names need not be recent or popular; for example,the names may include all email addresses, etc. captured for a user.Even old, rare names may be useful to identify. For example, if a userhas only sent or received a single message to a particular personseveral years ago, it may still be desirable to recall the message whenthe sender/recipient email address is recognized. In one embodiment, thenames are limited to recent and/or popular names to limit the amount ofdata required to store the names. To extract the name, the query system132 examines the user's email system and determines the names of usersto which the user recently or often sends email messages. In anotherembodiment, the query system also correlates this information with thesubject and or text of email or other correspondence. For example, if auser frequently sends email to a person, and the user also frequentlyrefers to the name of an organization with which the person isaffiliated (e.g., the company field of the person's contactinformation), the query system identifies the organization and contentof interest to the person.

The query system 132 may also extract keywords from a selection or froma clipboard buffer. A selection comprises the text or objects that arehighlighted in the currently active application. A clipboard typicallystores information that was previously selected and copied or cut by theuser.

Once the query system 132 has extracted keywords from a data stream, thequery system 132 generates a search query 206. The search query that thequery system generates may comprise keywords extracted from a singledata stream or may comprise keywords extracted from multiple strings.Whether a word extracted from more than one source continues to be usedin an implicit query may be determined in various ways. For example, ifthe word “budget” occurs with some frequency (e.g., fifty times) in adocument but the user has not recently typed the word budget, budget maycontinue to be included in a query generated by the query system 132.

In the embodiment shown in FIG. 2, the query system 132 next transmitsthe search query to a search engine, for example, search engine (122)208. In other embodiments, the query system 132 transmits the query toother search engines, for example, a search engine running on a serverdevice 150, such as the Google™ search engine. The search engine 122performs a search of one or more indices, either local or global, andprovides at least one article identifier as a result set. The querysystem receives the result set from the search engine 210. Once thequery system 132 receives the result set, the query system 132 mayperform additional functions or additional operations on the result set.For example, in one embodiment of the present invention, the querysystem 132 ranks the article identifiers in the result set based onrelevancy scores. The relevancy scores may be related to previous eventsthat were recorded by the query system 132 or another component or maybe based on other criteria.

Once the query system 132 has received the result set and ranked theresults or performed other operations, a query system 132 transmits theresult set to the display processor (128) 212. The display processor 128displays the result set to the user. The display processor 128 maydisplay the result set in a format similar to a format used for globalresult sets such as those provided by a search engine utilizing a globalindex, e.g., the Google™ search engine. The display processor 128 mayalternatively display the result sets in a small window superimposedover another application that the user is currently using. In oneembodiment of the present invention, the display processor 128 creates awindow based on the amount of available screen space on the usersdisplay and displays the result sets from the query system 132 in thewindow that it created. In another embodiment, the window of an activeapplication may be modified to include the result sets.

In one embodiment, once the desired number of results has been retrievedin a result set, the results are stored in memory, and the query system132 informs the display processor 128. In another embodiment, if thenumber of results in a result set is less that a pre-determined minimumnumber, the query system 132 executes additional queries to retrieveresults until the minimum threshold of results has been exceeded.

The query system 132 may execute a single query or may execute multiplequeries based on one or more data streams in order to return result setsthat are relevant to the current user context. FIG. 3 is a flowchartillustrating a method of executing multiple queries to return resultsrelevant to the user's context in one embodiment of the presentinvention. In the embodiment shown, the query system 132 receives thefirst event 302. The query system 132 extracts keywords from the event304. The query system 132 generates a first search query based on thekeywords extracted from the first event 306. The query system transmitsthe first query to a search engine 308. The search engine to which thequery system 132 transmits the search query may be a local or globalsearch engine application. For example, the search engine applicationmay comprise an application executing on the user's machine with accessto content located on the machine or available to the machine on a localnetwork or the search engine application may comprise a global searchengine application, such as the Google™ search engine. The query system132 then receives the first result set 310.

The query system 132 also receives a second event 312. The second eventmay comprise information from a second data stream. For example, in oneembodiment of the present invention the present system receives thefirst event from a document buffer such as a Microsoft® Word documentbuffer. The query system 132 receives a second data stream from theclipboard. The query system 132 extracts keywords from the second event314. The query system then generates a second search query based on thekeywords extracted from the second event 316.

The query system then transmits the second search query to a searchengine. As with the first search query, the query system 132 maytransmit the second search query to either a global or local index. Forexample, in one embodiment of the present invention, the first eventcomprises information related to data that is stored on the user's localmachine. Accordingly, the query system 132 transmits this search queryto the local search engine (122) 318. The second data stream comprisesinformation from the user's previously executed explicit query. In thiscase, the query system 132 transmits the search query to a global searchengine 170, such as the Google™ search engine. The query system 132 nextreceives the second result set from the second search engine 320. Aswith the first result set, the second result set comprises at least onearticle identifier or may comprise plurality of article identifiers,which are relevant to the second search query.

Once the query system 132 receives the first result set and the secondresult set, the query system 132 combines the two result sets 322. Thequery system may combine the result sets in a number of ways. Forexample, in one embodiment of the present invention, the query systemmerges the two result sets, the results being ranked by the relevancyscore regardless of from which query a particular result originated. Inanother embodiment of the present invention, the query system 132 takesinto account the search query that was executed in determining how tocombine the result sets. For example, the query system 132 may rateresults acquired from a local index higher than those results acquiredfrom a global index or vice versa. In such an embodiment, the resultsets that are shown to the user will have the local results listed abovethe global results. Embodiments of the present invention may performadditional combining and/or ranking steps here as well. For example inone embodiment of the present invention the query system 132 evaluatesthe list of article identifiers that are returned from both result setsand eliminates any duplicates. In yet another embodiment, the querysystem 132 combines the article identifiers from each of the two resultsets and then performs additional ranking, weighting, and sortingprocedures on each of the result sets. For example, an articleidentifier that appears in multiple result sets may receive a higherweighting than if it had appeared in only one result set. In oneembodiment, the result sets are not merged; they are displayed on onepage in separate lists. Once the query system 132 has combined theresult sets from the two search queries, the query system 132 transmitsthe combined result sets to the display processor (128) 224.

In one embodiment of the present invention, the query system 132combines keywords from the last several implicit queries that wereexecuted with keywords that originate from data streams from the user'scurrent application. For example, in one embodiment of the presentinvention, the query system 132 extracts keywords from each of the lastthree queries executed by the user. The query system 132 combines thekeywords that were extracted from the last three queries with keywordsfrom the buffer of the currently active application. The query system132 then executes a query for each set of keywords that are extracted,returning multiple result sets. The query system 132 then merges theresult sets in a manner similar to those described herein. The querysystem 132 transmits the top n (e.g., top 20) results to the displayprocessor 128 to be shown to the display to the user.

FIG. 4 is a flowchart illustrating a method for ranking a combinedresults set in one embodiment of the present invention. In theembodiment shown, the query system 132 receives a first result set at202. For example, the query system extracts a keyword from the buffer ofthe application the user is currently using and submits the search queryto a search engine such as local search engine 122, and in response, thesearch engine executes a query and returns a result set to the querysystem 132. The query system 132 then ranks the first result set basedon relevancy scores related to the user's current context state 204. Thequery system 132 receives a second result set 206. For example, thequery system may submit a query to a global search engine 170, such asGoogle™ Search Engine. The query system 132 receives the second resultset 206. The query system next ranks the second result set 208. In oneembodiment of the present invention, the local and global result setsare ranked based on the same set of criteria. In other embodiments, theresult sets originating from local and global indexes may be rankeddifferently based on user specified or other criteria. Those criteriamay include click-through data from the user.

The query system 132 attempts to create a combined result set. The querysystem 132 may perform this in a number of ways. For example, in theembodiment shown in FIG. 4, the query system identifies an articleidentifier that appears in both the first and second result sets 210. Anarticle identifier that appears in both the first and second result setsmay be more likely to be of interest to the user than an articleidentifier that appears in only one of the result sets. The query system132 creates a combined result set and adds the article identifier thatwas identified as being in both the first and second result sets to thecombined result set 212. The query system 132 may repeat the steps 210,212 to add additional article identifiers to the combined result sets.The query system 132 may perform additional types of methods in order toadd article identifiers to the combined result set. For example, thequery system 132 may extract the top ten article identifiers from eachof the first result set and the second result set and add those articleidentifiers to the combined result set. The query system may furthereliminate duplicates from the combined result set or perform otheroperations that are useful in creating a relevant combined result setfor the user. Once the query system 132 has created a combined resultset with the relevant article identifiers, the query system 132transmits the combined result set to display processor 214. The displayprocessor will then display the combined result set to the user.

In one embodiment of the present invention, the query system 132regularly (e.g., continually) retrieves real time events and contextualevents from the event queue. The real time events may comprise, forexample, an event indicating that the user has loaded a particular filefrom the file system. A contextual event may comprise, for example, thefact that a user has typed a particular word into the document. Thequery system 132 uses the events to update the user's state, e.g., thelist of most recently typed words, the current sentence, the currentselection, the text that the mouse is currently over, the current textin the clipboard, or the entire buffer of an active application. Thequery system may maintain user state separately for each activeapplication. For example, in one embodiment, the most recent words typedinto each application are maintained separately. The query system 132combines context for multiple applications (e.g., a word processing anda spreadsheet application). The query system 132 determines when andwhether to recompute queries and issue new queries when the contextchanges. For example, in one embodiment of the present invention, thequery system 132 attempts to recompute queries within 250 millisecondsof when the user pauses after a word. The query system 132 may recomputethe queries immediately upon certain events, e.g., when the user loadsthe file or loads a web page. Query system 132 then constructs a queryusing the user's context such as the most recently typed words. Thequery system 132 may also construct a query string using special namessuch as the “To:” recipient of an email message that the user iswriting, or the “buddy” name of another user with whom the current useris having an instant messenger conversation. The query system 132 sendsthe query string to the query processor.

FIG. 5 is a flowchart illustrating the method for processing a query inone embodiment of the present invention. In the embodiment shown, thequery system 132 first receives the query string 502. The query system132 may receive the query string in a number of ways. For example, inone embodiment, the query system 132 receives an API call with a querystring created via one of the two methods described herein. The querysystem 132 then sends the raw string to the full text index. Inresponse, the query system 132 receives a list of ranked event IDs 506.

Using the list of ranked event IDs, the query system 132 retrieves theresult set at 208. For example, in one embodiment of the presentinvention, the query system 132 iterates over the list of events untilenough results are retrieved to provide a query that will result in aresult set sufficient for display to a user. During the process ofiteration, the query system 132 retrieves the event record from the datastore 140, which includes information such as the type of event andlocation (e.g., the URL, path, or other location attribute).

An embodiment of the present invention may filter results received fromthe local or global index. The results may be filtered based on avariety of parameters. For example, in one embodiment, if the result setthat the query system 132 finds does not meet the query restrictions,the query system moves to the next result. This step is referred to asthe query syntax filter. If the result found does not match a displayrestriction (e.g., email only), the query system skips to the nextresult. This step is referred to as the display parameters filter.

In another embodiment, the results are filtered based on parameters ofan article in a result set. The query system 132 in such an embodimentreceives the URL, site, user, cache date, and file type of each of thereturned results. The results may be filtered by the query system 132 ormay alternatively be filtered by the event database. Which componentfilters the results depends on the full text index used by theembodiment of the invention and also depends on other criteria such asthe storage space required and the effect on the query time. The displayparameters that may be filtered include file type and number of results.

The query system 132 filters the results after receiving n results fromthe full-text index. The query system ranks results and compares theresults against the filters. If the result does not pass the filters,the result is eliminated from the results that will be returned to theuser.

In one embodiment of the present invention, the query system 132generates snippets to be displayed with or in place of a link and title.In one such embodiment, when the query system 132 indexes a web pagedocument, e.g., when the user loads the document, the query system 132retrieves document context, including, for example, the first onehundred kilobytes of document text. The query system 132 scans thedocument context for script tags. It removes the script begin tag, thescript end tag, and all text in between the two tags. It then does thesame for the style tags, removing the style begin tag, the style endtag, and all text between those two tags. Next, the query system 132removes other tags that are within less than and greater than (<>)symbols. As the query system 132 iterates over the words in the text,the query system 132 builds a list of positions where the search termsare located within the document. The query system stores the informationin the data store.

Once the query system 132 has retrieved the results and created asnippet for each of the results in the result set, the query system 132reorders results based on information from the document records, such asthe frequency of access. In other embodiments, other methods are used toreorder the result set. In one embodiment of the present invention, thequery system 132 may also store a filter list—details regarding whichfilters were applied and/or what the results of filtering were—inmemory. By storing the filter list in memory, the query system 132 isable to subsequently access the list to determine relevant results forthe user's context state and for other purposes, e.g., in one embodimentthe query system 132 accesses the filter list or the memory in order todetermine a relevancy score for results appearing in newly retrievedresult sets.

The query system 132 next transmits the results to the display processor514. The display processor 128 then processes the results into a formatthat can be displayed to the user. For example, in one embodiment of thepresent invention, the display processor 128 processes and transmits theresult set as an HTML document. IN another embodiment, the displayprocessor 128 processes and transmits the result set as an XML document.

In one embodiment of the present invention, the query system 132 formsmultiple query strings and combines results of the queries to determinewhat inputs are most likely to provide relevant results. For example, inone embodiment of the present invention, the query system 132 utilizes asentence extracted from a Microsoft® Word document. For example,consider the following sentence: “What is the budget for the secondquarter of 2003?” Not all the words that appear in this sentence arenecessary for a search query. For example, many of the words in thesentence are filler or “stop” words. Filler words include words such as“the” which are determiners and are not necessarily relevant to anyparticular query. These words may be filtered out before the searchquery is submitted to the search engine 122. The original sentence maybe maintained to compare to future content extracts.

An embodiment of the present inventions is able to use various measuresto determine which results to show to the user. For example, in oneembodiment of the present invention the click-through rate for aparticular article may be used to determine whether or not a keywordshould be thought of as relevant, e.g., if the user clicks on aparticular result in a content display window, the keyword used tolocate the result may be more relevant than other keywords. Anembodiment of the present invention is able to extract keywords frommultiple strings, thus providing a more complete view of the user'scontext. For example, in one embodiment, keywords are extracted from anemail message and a word-processing document. The message and documentmay be closely related, somewhat related, or unrelated; by utilizingboth sources for keywords, the embodiment is able to either rate ashared keyword more highly or provide additional keywords. An embodimentof the present invention is able to use user activity to trigger theexecution of a query for context relevant data. For example, in oneembodiment, when a user completes a sentence in a document by typing apunctuation mark, query execution is triggered.

Embodiments of the present invention may present the user with clearerresults in a format similar to the result sets that are provided bygenerally available search engines such as the Google™ Search Engine.The results include links that the user can click on to see the currentcopy of the document in its original form (e.g., PDF) or a cached copy,which is stored in a processed format (e.g., HTML). For example, theuser may wish to see an HTML version or the original version may not beavailable in a repository any longer. The results also include a snippetto indicate to the user why the results are included in the result set.The result set also includes information for a particular article suchas the size, the time the article was last accessed, and the frequencyof access of the article. The results may contain a picture which iseither a representative image selected from the document content if oneexists; a screen shot of the document or another image that helps theuser to understand why the article is included in the results. The querysystem returns objects that are on a results page. The display processor128 completes the formatting of the page. The exact formula rule set mayvary based on implementation of a particular embodiment of the presentinvention.

The query system 132 creates query strings based on a user's currentcontext. By creating queries based on the user's current context, thequery system 132 saves the user time by producing results withoutrequiring the user to type in the explicit query. The user may beinterested in these results but may not have typed in the queryexplicitly for a number of reasons. For example, a user may not rememberthat he wrote an email related to the current context. However, once theemail is presented to the user and the content is displayed, the usermay exhibit an interest in the article. Another example is that the usermay recall having performed a similar task before, but may not recallenough information to actually perform an explicit search. Therefore,the query system 132 provides information to the user that the user mayotherwise not be able to, or might not, access.

A query system filters results and generates snippets and then storesthe query results in memory. For explicit results, the user makes arequest via a web browser, and the results are returned via an HTTPserver, which transmits the results to the web browser as an HTTPresponse. In contrast, for implicit query system 132 queries, the querysystem notifies the display processor that new results are ready and thedisplay processor makes an HTTP request to the HTTP server to retrievethe results.

In one embodiment, once a query system has results from each of thesequeries, the query system merges them into a single list giving weightto the relevance score based on which list they come from. For example,in one embodiment, query results are based on the recently typed words,which are weighed more heavily than results based on words from theentire document. For example, one embodiment uses factors such as howfrequently the words appeared in a document, the document frequency ofthe words, and how long the foreground application has been in theforeground in order to determine the relevancy of the generated querystring.

The query system 132 may combine the relevance score of the results tothe query string with the relevance of the query string itself. Thequery system 132 then filters out results below a relevance thresholdscore. Based on user's click-through data, the filter may also beadjusted. In such an embodiment, the query system 132 attempts to strikea balance between showing a user too few results, resulting in a blankcontent display window that is not useful to the user, and showing toomany results, resulting in a high noise-to-signal ratio that issimilarly not useful.

An embodiment of the present invention may use various parameters todetermine when to show a user a new result set. In one embodiment,various queries are constructed and executed at different rates. Forinstance, in one embodiment, the query system 132 updates a query basedon the most recently typed words after the user has typed n new words(e.g., 10 or 20). In contrast, the query system 132 in such anembodiment constructs and executes a new query based on a completedocument buffer when either the document is opened or the window inwhich the document is displayed is brought into focus. Various othertypes of measures may also be used, including, for example, the entry ofa punctuation mark.

In one embodiment of the present invention, the query system 132utilizes a relevance metric to filter out results that are below athreshold of relevance. A user may be willing to accept a higher levelof noise in relation to an implicit query than they would tolerate withregards to an explicit query, i.e., the user is provided with resultswithout having to perform the step of issuing an explicit query and,therefore, may be more willing to accept results that are not asrelevant to the context of the user as the explicit results are relevantto the explicit query. On the other hand, the user is sacrificing screenreal estate in order to display the contents display comprising theimplicit query results. Accordingly, in one embodiment, an assumption ismade that the implicit results should be relevant, at least to somedegree, to the user's current context state. Thus, different and evencontradictory assumptions may be made about relevancy of implicit versusexplicit results in various embodiments of the invention.

FIG. 6 is a flowchart illustrating a method for identifying terms in oneembodiment of the present invention. In the embodiment shown in FIG. 6,the query system 132 receives an event 602. The event may comprise, forexample, an event signifying that a user has typed a sentence in adocument. The query system 132 extracts text from the event 604. Thequery system 132 then identifies parts of speech in the text of theevent 606. Parts of speech include, for example, nouns and verbs. Thequery system 132 may also identify names within the content by comparingthe parts of speech to previously stored names or by using processingrules. Automatic part of speech tagging is known. See, e.g., Brill,Eric; A Simple Rule-Based Part Of Speech Tagger; Proceedings of ANLP-92,3rd Conference on Applied Natural Language Processing (1992). Variousstatistical and rule-based techniques may be utilized. For example, inone embodiment, a word is initially categorized as a verb based on astored corpus. The categorization may then be modified based on thecontext in which the word appears, on a property of the word, or basedon other observed properties. For example, proper nouns are generallycapitalized; if a word that generally functions as a verb is capitalizedin the middle of sentence, it may be functioning as a noun in thatsentence.

The query system 132 next generates a search query for one or more partsof speech 608. For example, the query system 132 may generate one searchquery for nouns and another for proper nouns. The query system 132transmits the search query to at least one search engine, which may beeither local or global 610. The query system 132 then receives searchresults resulting from the search queries 612. In the embodiment shown,when the query system 132 receives results from all of the submittedqueries, the query system 132 combines the result sets 614. The resultsets may be combined in various ways as described herein. In oneembodiment, the query system 132 weights results associated withdifferent parts of speech in different ways, e.g., noun-related resultsmay be weighted more heavily than verb-related results.

FIG. 7 is a flowchart illustrating a method for altering a relevancescore in one embodiment of the present invention. In the embodimentshown, the query system 132 receives at least two result sets 702. Thetwo result sets are received in response to the issuance of two queries,for example, the noun-related and verb-related queries discussed inrelation to FIG. 6 above. The query system 132 merges the result sets tocreate a merged result set 704. For example, the result sets may includearticle identifiers that are common to the result sets. If so, the querysystem 132 eliminates the duplicates when creating the merged resultset. The query system 132 transmits the merged result set to the displayprocessor 128.

The display processor 128 presents the user with the merged result set.The display processor 128 may present the merged result set in a formatsimilar to that used by conventional web search engines, such as theGoogle™ Search Engine. Other formats may be used. For example, in oneembodiment, each article identifier in the merged result set includes ahyperlink. The user is able to click on the hyperlink to indicateinterest in the hyperlink. In the embodiment shown, the user clicks on ahyperlink and causes a signal to be transmitted to the query system 132.

The query system 132 receives the signal indicating an interest in thehyperlink 708. In response, the query system 132 searches the data store140 to determine the source of the article identifier in the mergedresult set 710. The query system 132 increases the relevance score ofthe source of the query 712.

For example, the query system 132 receives three result sets and mergesthe result sets to create a merged result set. The query system 132stores the source of each of the article identifiers, in memory or inthe data store 140, for example. The query system 132 then receives thesignal indicating interest in a particular article identifier andsearches the data store 140 to determine what the source or sources ofthe article identifier was, i.e., from which query or queries did thearticle identifier originate. The query system 132 identifies the firsttwo queries as having resulted in the article identifier that the userclicked. The query system 132 increases a relevance score for the querysources when subsequent queries are executed. Accordingly, in theresults from subsequent queries, the results from the sources that theuser has previously shown interest in will appear higher in the listthat is shown to the user. This may facilitate presenting the user withrelevant results. A query source can be (a) an application (e.g., a wordprocessor), (b) a type of query (e.g., text nearby the cursor position,recently typed words, or recognized items, such as email addresses), (c)a type of event, such as receiving an email or opening a document, (d) akeyword, (e) an article type filter or restriction (e.g., .doc files),or (f) combinations thereof. As an example, based on click-through data,the system may determine that a query for a name is particularlysuccessful for a particular user when the user receives an instantmessage referring to the name.

In one embodiment of the present invention, a content display isdisplayed on a user interface. The content display comprises one or morearticle identifiers that are associated with a search query. Forexample, the query system 132 may derive and submit an implicit searchquery based on the user's contextual state. The query system 132displays the results associated with the implicit query in a contentdisplay. In one embodiment, the query system 132 periodically (e.g.,every two seconds) generates another implicit query based on the user'sthen current contextual state. The query system 132 then determineswhether or not to update the content display to provide the new results.The query system 132 may determine that the currently displayed resultsneed not be replaced. For example, in one embodiment, the query system132 compares the previous query to the current query. If the queriesinclude many or all of the same terms, the query system 132 does notupdate the content display with the new results.

For example, in one embodiment, a user may be editing a word processingdocument when a first implicit query is generated. The implicit querymay be based on various attributes of the document and other parametersas described herein, such as for example, text recently typed by theuser. The query system 132 displays the results of the implicit query ina content display appearing on the right side of the user's display. Ifuser continues to edit the document and makes minor changes to thedocument. Some time (e.g., five seconds) after the first implicit queryis executed, the query system 132 generates a second query based on theuser's contextual state. At this point, the query system 132 may comparethe two queries to determine whether or not to execute the second queryor may cause the second query to be executed. When the results set isreturned, the query system 132 compares the result set from associatedwith the latest query to the result set currently displayed in thecontent display. Since in this example the user is editing one documentin a single application, the user's contextual state may not havechanged significantly. Thus, the result set may not have changedsignificantly and it may be more distracting than useful to display thenew results.

Using the same example, assume that the user next activates an emailapplication and reads a first email message. The query system 132generates a third implicit query. The system may now consider to theuser's contextual state to have changed significantly, since the user isinteracting with a different application altogether. As such, the thirdresult set associated with the third query may vary substantially fromthe currently displayed result set (i.e., the result set from the firstquery in this example). If some aspect of the result set does varysufficiently, e.g., the number of articles in the third result that arenot in the currently displayed result set is greater than apredetermined threshold, the query system 132 refreshes the contentdisplay, displaying the third result set in place of the first resultset.

In embodiments of the present invention, the query system 132 maydetermine whether or not to update the content display in various ways.For example, the query system 132 may compare a current search query toa previously executed search query. In one embodiment, the query system132 a result set associated with a recently executed query to a resultset associated with a query executed prior to the recently executedquery. If the result sets are substantially different, the new morerecent results replace the prior results, in whole or in part. Thedifference between the results may be determined using various factors.For example, the set of article identifiers may be compared. If thearticle identifiers are all or substantially the same, the query system132 may not update the content display.

In one embodiment, the query system 128 compares the difference inresult sets to a threshold to determine whether or not to refresh acontent display. FIG. 8 is a flowchart illustrating a method of varyinga refresh threshold for a content display in one embodiment of thepresent invention. In the embodiment shown, the query system 132 makes adetermination of whether or not to display new results in a contentdisplay based on a refresh threshold. If the difference between thenumber, type, ranking, or other property of the article identifiers inthe new result set and the same property for article identifiers in thenew result set is lower than the threshold, the result set is notdisplayed. One reason for not showing the new result set is to maintainconsistency in the user interface to avoid distracting the user. Thethreshold may vary based on user activity. For example, in theembodiment shown, the query system 132 receives a signal indicating aninterest in an article identifier by the user 802. In response, thequery system 132 lowers the threshold for display of the new result set804. In other words, the new result set may be more similar to thecurrently displayed list than before the threshold change and still beshown to the user since the user is indicating an interest in theinformation provided in the content display window. The query system maytime the updating of the result set in relation to other events, forexample, updating the result set around the same time as a user browsesa new web page, as opposed to updating the result set at a random time,which may be more distracting to the user.

The query system 132 receives the new result set 806. The query system132 then calculates a measure of difference from the existing resultset, i.e., the currently displayed result set 808. The measure ofdifference may comprise a relatively simple calculation, e.g., thenumber of article identifiers that are in both the existing result setand the new result set. The measure may instead comprise a morecomplicated measure. For example, the query system 132 may calculate arelevance rating for both result sets and then compare the difference inrelevancy ratings to a relevancy threshold. If the measure is greaterthan the threshold 810, the query system 132 transmits the new resultset to the display processor 128 for display 812, and the process ends814. If the measure is less than or equal to the threshold, the queryprocessor does not transmit the result set to the display processor, andthe process ends 814.

FIG. 9 is a flowchart illustrating a method of varying a relevance scorefor a result based on a user's click-through behavior in one embodimentof the present invention. In the embodiment shown, the query system 132receives a signal indicating an interest in content displayed in thecontent display window 902. The query system 132 evaluates the interestsignal to determine the content type associated with the content andstores the content type in a data store 904. The content type maydefine, for example, the file type of the content. The file type may bea web page, .PDF file, image, email, word processing document,spreadsheet document, text file, or any other suitable content type. Thequery system then determines and stores information indicating thesource of the content associated with the interest signal 906. Thesource may comprise, for example, the web, a local machine, or othersource of data. In one embodiment, the source comprises the search queryused to retrieve the results. In the embodiment shown, the query system132 also stores the keyword or keywords associated with the interestsignal 908. In another embodiment, additional attributes of the content,which are associated with the interest signal, are also stored. Theprocess of receiving an interest signal and storing attributesassociated with the interest signal may be repeated multiple times, suchas each time the user clicks on a link in the content display window orother events occur as described herein.

The query system 132 subsequently receives a search query 910. The querysystem 132 transmits the search query to a search engine. 912. Thesearch engine returns a result set in response to the search query,which the query system 132 receives 914. The result set comprises one ormore article identifiers. The article identifiers are associated with arelevance score. The query system 132 uses the click-through data storedin the data store 140 to modify the relevancy score of each of thearticles identifiers in the result set based on the type of content, thesource of the content, and the keywords used in the search query.

In another embodiment, the query system 132 receives multiple queriesafter storing the content type, source, keyword(s), and otherinformation. When the query system 132 receives the multiplecorresponding result sets, the query system 132 uses the previouslystored click-through data to adjust the relevancy scores both within andacross result sets before displaying the combined result set to theuser.

One embodiment of the present invention utilizes content type, source,keyword, and other data related to items that the user did not click on.The query system 132 of one such embodiment reduces the relevancy scoreof article identifiers corresponding to content types and sources thatthe user has not clicked as frequently as other types of content.

For example, in one embodiment, the user views a web page and edits adocument. Four queries are generated from the user context. The firstquery comprises information from the web page. The second querycomprises the last ten words that the user types. The third querycomprises the sentence that the user just pasted in the document. Andthe fourth query comprises the words that the user is currentlyselecting with the mouse. The query system 132 submits the queries toone or more search engines and receives four result sets in response.The query system 132 merges the results and presents the first tenresults to the user in a content display window.

The user clicks on the second result in the content display window. Thesecond result was present in the result set from the first query and theresult set from the third query. In the first query, the result wasranked first; in the third query, the result was ranked fifth. In suchan embodiment, the sources that lead to the result that the userselected are boosted to provide positive reinforcement proportional tohow much each contributed to the result being shown. The content type,source, keywords, and other attributes associated with the result allmay be boosted so that in future queries, those types of results aremore likely to be shown to a user.

FIG. 10 is a flowchart illustrating a method for displaying implicitquery results in one embodiment of the present invention. In theembodiment shown, the query processor receives an event triggeringexecution of a new query 1002. For example, the user may have entered apunctuation mark at the end of a sentence, or the user may have receivedan instant message. In response, the query system 132 executes a newquery 1004. The query system 132 subsequently receives the results ofthe query 1006.

The query system 132 (or display processor 128) next decides whether ornot to display the new query results in the content display. The querysystem 132 may use any number of criteria to decide whether or not toshow the new results. In the embodiment shown, the query system 132determines whether or not the user 112 a is active in the contentdisplay window 1008. The query system 132 may determine that the user112 a is active in the window by, for example, determining the currentmouse position. The query system 132 (or display processor 128) candetermine the current cursor position by querying the operating systemand comparing the cursor position to the area covered by the contentdisplay window. The operating system may also be able to report thecurrently active window. If the content display window is active, thequery processor 132 pauses for some period of time (e.g., two seconds)1009, and then repeats the determination of whether or not the contentdisplay is the active window 1008. In another embodiment, the querysystem 132 also pauses if the cursor is moving towards the contentdisplay window, evidencing the user's possible intent to interact withcontent displayed there.

If the content display window is to be updated, the query processor 132determines the difference between the results returned from the latestquery and the results currently displayed in the content display andcompares the difference to a threshold 1010. The query processor maydetermine the difference in numerous ways. For example, the queryprocessor 132 may determine how many article identifiers appear in thenew result set and are not present in the currently displayed resultset. The query system 132 may also or instead determine how much overlapexists between the keywords in the query or queries that provided thecurrently displayed content versus the keywords in the query or queriesthat provided the new results. In another embodiment, the keywordscomprise categories, and the query system 132 evaluates overlap betweenthe categories. If the difference measure is less than a thresholdmeasure, the process ends 1016. In other words, if the new result setand the currently displayed result set are very similar, then showingthe new result set is not necessary and may even be unnecessarilydistracting to the user 112 a. Accordingly, if the difference betweenthe new result set and the currently-displayed result set is not greaterthan a pre-determined threshold, the new result set is not shown to theuser 112 a.

In the embodiment shown, if the difference between the new result setand the currently-displayed result set is greater than a thresholdmeasure, the query processor 132 determines whether the relevance of thenew result set to some aspect of the user's data or actions is greaterthan that of the currently-displayed result set 1012. If not, theprocess ends 1016; the new results are not shown to the user 112 a. Ifthe relevance measure of the new result set is greater than that of thecurrently displayed result set, the query processor 132 causes the newresult set to be displayed 1014. The process then ends 1016. The processshown in FIG. 10 may be repeated in order to ensure that the contentdisplay continues to show results relevant to what the user's contextualstate. For example, the process shown may repeat every ten seconds orother suitable time interval.

Other methods of determining when to update the content display windowmay also be used. For example, in one embodiment of the presentinvention, the query system 132 causes less relevant results to bedisplayed if a certain time period has passed. In such an embodiment, itmay be assumed that if the user has not clicked on the content in thecontent window display, then other content may prove more relevant eventhough the initial relevancy scores of the content are lower than whatis currently displayed. In another embodiment, only a portion of thecurrently displayed results is replaced by content that seems to be lessrelevant.

In one embodiment of the present invention, the queries themselves arenot run if the user is not active and if the user's system is notactive. For example, in one such embodiment, the user stops typing orinteracting with the user interface for a relatively long time period(e.g., five minutes). During those five minutes, no other activity,including for example the reception of email, occurs. Based on therelative lack of activity, the query system 132 pauses and does notexecute queries based on the user's contextual state. When the userbegins interacting with the interface again or when other actions occur,such as the reception of an email, the query system 132 beginssubmitting contextual queries and causing the output of correspondingresults. In another embodiment, the query system 132 pauses even whenemail is received. If the user is idle for a certain period of time, hemay be away from the machine, and it may be preferable not to executeimplicit queries so as to not cause the user's programs to be swappedout of memory.

In one embodiment of the present invention, a user profile providesattributes, which may be used by the query system 132 to modify searchqueries, and/or result sets to provide the user with a personalizedexperience. Attributes stored in the user profile that may affect searchqueries and/or the ranking of results sets include information such aspeople to whom or from whom a particular user sends or receives email orchat messages, words that appear often in the user's explicit searches,words the user often types, words that appear frequently in the user'sdocuments, and other types of attributes specific to the user. FIG. 11is a flowchart illustrating a method according to one embodiment of thepresent invention for modifying a search query and the correspondingresult set based on the search query, the modifications based at leastin part on an attribute stored in a user profile. In the embodimentshown, the user 112 a enters a search query. The query system 132receives the search query 1102. The query system 132 determines whichuser submitted the search query 1104. For example, a user identifier maybe included with the search query. Alternatively, the query system 132may determine the user by calling an operating system function call todetermine the user currently logged into the computer.

The query system 132 utilizes the identity of the user to retrieveattributes from the user profile 1106. The user profile may be stored inthe data store 140. Alternatively, the user profile may be stored on aserver device, such as server device 150, in communication with thenetwork 106 and thereby accessible to the query system 132.

The user profile may store a plurality of attributes that are userspecific. For example, the user profile may store the preferred size andplacement of a content display window for a particular user. The userprofile may also store preferred terms and search methods for a user.The attributes in the user profile may be set explicitly by the user ormay be set implicitly, without express entry thereof by the user, basedon actions of the user, the time of day, or any other event or actionsthat may be tracked by the computer system.

In the embodiment shown, the query system 132 receives at least twoattributes from the user profile; a first attribute that is searchrelated, and a second attribute that is result set related. The querysystem 132 uses the first attribute to modify the search query 1108. Thequery system 132 then submits the search query to a search engine 1110.For example, the user may enter a term followed by a punctuation mark ina document, triggering an implicit search query. When the query system132 receives the search query, the query system 132 modifies the searchquery and submits it to a search engine rather than submitting thesearch query directly to the search engine.

The query system 132 receives the search result set from the searchengine 1112. The result set comprises one or more article identifiers.The article identifiers may be sorted in any of a number of ways. Thequery system 132 uses a second attribute from the user profile to rankthe results 1014. For example, the user 112 a may prefer results from aparticular website over results provided from any other source. If theuser 112 a has such a preference, the query system sorts the result setbased on that preference.

In one embodiment of the present invention, the query system 132 storesimportant terms, including for example, the names of people with whomthe user often communicates, in memory. These names may come fromrecipients or senders or emails or instant messenger “buddy” names, forexample. In such an embodiment, the user profile may not be provided.The query system uses the important terms stored in memory to affect thecreation of search queries and/or the ranking of result sets.

One embodiment of the present invention generates implicit queries basedon a portion of the text in a document or article in which the user isshowing an interest. Showing an interest may comprise typing in thedocument, printing the document, sending the document (e.g., via fax oremail) or any other action that may be used to indicate an interest. Onesuch embodiment is able to differentiate between boilerplate present inthe article and content; the content is what is used for generating theimplicit query.

FIG. 12 is a flowchart illustrating a method for identifying boilerplateand content in one embodiment of the present invention. In theembodiment shown, the query system 132 first receives a signaltriggering implicit query creation 1202. This signal may be generated inresponse to typing by the user 112 a. For example, the user 112 a maytype a sentence ending in a period. In response to receiving the signal,the query system 132 identifies a section of the article in which theuser 112 a is showing an interest 1204. The section may comprise thewhole document or a portion of the document.

Within the section that the query system 132 identifies, the querysystem 132 evaluates the text or other elements to identify boilerplate1206. For example, in one embodiment, the query system 132 determinesthat any text following the word “copyright” is boilerplate. Other typesof boilerplate include, for example, navigational text, disclaimers, andtext that appear on every page of a web site. The query system thenidentifies content within the article 1208. The article may compriseonly content and boilerplate or may include additional elements, such ascontrols, which are not used in generating a query. In one embodiment,the attributes of button in the article are used to generate an implicitquery.

Once the query system 132 has identified content in the article, thequery system 132 generates an implicit search query 1210. The querysystem 132 then submits the search query to a search engine 1212.

The foregoing description of embodiments of the invention has beenpresented only for the purpose of illustration and description and isnot intended to be exhaustive or to limit the invention to the preciseforms disclosed. Numerous modifications and adaptations thereof will beapparent to those skilled in the art without departing from the spiritand scope of the present invention.

1. A method comprising: identifying a common element in a plurality ofarticles; determining whether the common element is a boilerplateelement of the articles; assigning a weight to the common element basedat least in part on whether the element is a boilerplate element;establishing a search result set including one or more of the pluralityof articles in response to a search query; and ranking the articlesincluded in the search result set responsive at least in part to theweight assigned to the common element.
 2. The method of claim 1, whereindetermining whether the common element is a boilerplate elementcomprises: analyzing a link associated with the common element in anarticle of the plurality of articles, wherein analyzing the linkassociated with the common element comprises analyzing an address towhich the link refers; and determining whether the common element is aboilerplate element of the article based at least in part on the linkassociated with the common element.
 3. The method of claim 1, whereinthe common element comprises one or more terms common to the pluralityof articles and wherein determining whether the common element is aboilerplate element comprises: analyzing a frequency of occurrence ofthe common terms; and determining whether the common terms are aboilerplate element responsive at least in part to the frequency ofoccurrence.
 4. The method of claim 3, wherein the common terms aredetermined to be a boilerplate element responsive at least in part tothe terms having a low inverse document frequency measure.
 5. The methodof claim 1, wherein determining whether the common element is aboilerplate element comprises: determining whether a term within thecommon element is within a predetermined list of terms; and determiningthat the common element is a boilerplate element responsive at least inpart to the term being within the predetermined list of terms.
 6. Themethod of claim 1, wherein determining whether the common element is aboilerplate element comprises: analyzing terms within the common elementto determine whether the common element is a content element or aboilerplate element; wherein the assigning assigns weights to commonelements giving content elements higher weight than boilerplateelements.
 7. The method of claim 1, wherein the weight assigned to thecommon element indicates a probability that the element is a boilerplateelement.
 8. A non-transitory computer-readable storage medium storingexecutable program code comprising code for: identifying a commonelement in a plurality of articles; determining whether the commonelement is a boilerplate element of the articles; assigning a weight tothe common element based at least in part on whether the element is aboilerplate element; establishing a search result set including one ormore of the plurality of articles in response to a search query; andranking the articles included in the search result set responsive atleast in part to the weight assigned to the common element.
 9. Thecomputer-readable storage medium of claim 8, wherein determining whetherthe common element is a boilerplate element comprises: analyzing a linkassociated with the common element in an article of the plurality ofarticles, wherein analyzing the link associated with the common elementcomprises analyzing an address to which the link refers; and determiningwhether the common element is a boilerplate element of the article basedat least in part on the link associated with the common element.
 10. Thecomputer-readable storage medium of claim 8, wherein the common elementcomprises one or more terms common to the plurality of articles anddetermining whether the common element is a boilerplate elementcomprises: analyzing a frequency of occurrence of the common terms; anddetermining whether the common terms are a boilerplate elementresponsive at least in part to the frequency of occurrence.
 11. Thecomputer-readable storage medium of claim 10, wherein the common termsare determined to be a boilerplate element responsive at least in partto the terms having a low inverse document frequency measure.
 12. Thecomputer-readable storage medium of claim 8, wherein determining whetherthe common element is a boilerplate element comprises: determiningwhether a term within the common element is within a predetermined listof terms; and determining that the common element is a boilerplateelement responsive at least in part to the term being within thepredetermined list of terms.
 13. The computer-readable storage medium ofclaim 8, wherein determining whether the common element is a boilerplateelement comprises: analyzing terms within the common element todetermine whether the common element is a content element or aboilerplate element; wherein the assigning assigns weights to commonelements giving content elements higher weight than boilerplateelements.
 14. The computer-readable storage medium of claim 8, whereinthe weight assigned to the common element indicates a probability thatthe element is a boilerplate element.
 15. A computer system comprising:a non-transitory computer-readable storage medium storing executableprogram code comprising code for: identifying a common element in aplurality of articles; determining whether the common element is aboilerplate element of the articles; assigning a weight to the commonelement based at least in part on whether the element is a boilerplateelement; establishing a search result set including one or more of theplurality of articles in response to a search query; and ranking thearticles included in the search result set responsive at least in partto the weight assigned to the common element; and a processor forexecuting the program code.
 16. The computer system of claim 15, whereindetermining whether the common element is a boilerplate elementcomprises: analyzing a link associated with the common element in anarticle of the plurality of articles, wherein analyzing the linkassociated with the common element comprises analyzing an address towhich the link refers; and determining whether the common element is aboilerplate element of the article based at least in part on the linkassociated with the common element.
 17. The computer system of claim 15,wherein the common element comprises one or more terms common to theplurality of articles and determining whether the common element is aboilerplate element comprises: analyzing a frequency of occurrence ofthe common terms; and determining whether the common terms are aboilerplate element responsive at least in part to the frequency ofoccurrence.
 18. The computer system of claim 17, wherein the commonterms are determined to be a boilerplate element responsive at least inpart to the terms having a low inverse document frequency measure. 19.The computer system of claim 15, wherein determining whether the commonelement is a boilerplate element comprises: determining whether a termwithin the common element is within a predetermined list of terms; anddetermining that the common element is a boilerplate element responsiveat least in part to the term being within the predetermined list ofterms.
 20. The computer system of claim 15, wherein determining whetherthe common element is a boilerplate element comprises: analyzing termswithin the common element to determine whether the common element is acontent element or a boilerplate element; wherein the assigning assignsweights to common elements giving content elements higher weight thanboilerplate elements.
 21. The computer system of claim 15, wherein theweight assigned to the common element indicates a probability that theelement is a boilerplate element.